Information theoretic clustering of the human pangenome minigraph

IF 3.9 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Letters Pub Date : 2025-03-12 DOI:10.1016/j.patrec.2025.03.004
Renato Ferrero , Filippo Gandino , Anna Carbone
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Abstract

Information theoretic clustering, long-range correlation, power-law scaling and self-similarity concepts have been broadly adopted for characterizing genomic features such as nucleotide composition, flexibility and bending. In this work, the 24 chromosomes of the human pangenome minigraphs, recently assembled by the Human Pangenome Reference Consortium (HPRC), are investigated to check to what extent self-similarity and scaling features are preserved in comparison to the reference linear sequences of the T2T-CHM13 individual. By taking the nucleotide self-similarity of the reference chromosomes as benchmark, it is shown that the pangenome minigraph segments exhibit lower self-similarity of the nucleotide composition compared to the linear sequence. The proposed information measures can be adopted to quantify the nucleotide self-similarity patterns and complement standard alignment techniques towards the coherent definition of the genomic profile of each species.
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Pattern Recognition Letters
Pattern Recognition Letters 工程技术-计算机:人工智能
CiteScore
12.40
自引率
5.90%
发文量
287
审稿时长
9.1 months
期刊介绍: Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition. Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.
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An efficient solution for GPUs to the ST-connectivity problem on dynamic graphs SR-LBSCC: Super resolution based screen content image compression at low bitrate MSNet: Multi-task self-supervised network for time series classification Information theoretic clustering of the human pangenome minigraph Editorial Board
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